(b) Find the equation of the least squares regression line. (Round your answers to four decimal places.) ý = 78.077 x + .391 Based on this line, what would you predict acrylamide concentration (in micrograms per kg) to be for a frying time of 240 seconds? What is the residual (in micrograms per kg) associated with the observation (240, 190)? (Round your answers to two decimal places.) predicted value micrograms per kg residual micrograms per kg

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
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Chapter10: Statistics
Section10.5: Comparing Sets Of Data
Problem 26PFA
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I need help with part b

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A STA 1053 HW10 Module10 - STA 1053.003 Spri...
b Acrylamide is a chemical that is sometimes f...
calculator - Google Search
+
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(b) Find the equation of the least squares regression line. (Round your answers to four decimal places.)
= 78.077 x +
.391
Based on this line, what would you predict acrylamide concentration (in micrograms per kg) to be for a frying time of 240 seconds? What is the residual (in micrograms per
kg) associated with the observation (240, 190)? (Round your answers to two decimal places.)
predicted value
micrograms per kg
residual
micrograms per kg
(c) Look again at the scatterplot from part (a). Which observation is potentially influential? Explain the reason for your choice. (Hint: See Example 4.9.)
The observation (300, 135) is potentially influential because that point has an x value that is far away from the rest of the data set.
The observation (240, 190) is potentially influential because that point has an x value that is far away from the rest of the data set.
The observation (300, 135) is potentially influential because that point has a large residual.
The observation (150, 150) is potentially influential because that point has a large residual.
The observation (150, 150) is potentially influential because that point has an x value that is far away from the rest of the data set.
(d) When the potentially influential observation is deleted from the data set, the equation of the least squares regression line fit to the remaining five observations is
= -44 + 0.83x. Use this equation to predict acrylamide concentration (in micrograms per kg) for a frying time of 240 seconds.
155.2
micrograms per kg
Compare this prediction to the prediction made in part (b).
Transcribed Image Text:webassign.net Content A STA 1053 HW10 Module10 - STA 1053.003 Spri... b Acrylamide is a chemical that is sometimes f... calculator - Google Search + 350г 350г 300 300 250 250 200 200 150 150 100 100 50 100 50 100 150 200 250 300 350 150 200 250 300 350 (b) Find the equation of the least squares regression line. (Round your answers to four decimal places.) = 78.077 x + .391 Based on this line, what would you predict acrylamide concentration (in micrograms per kg) to be for a frying time of 240 seconds? What is the residual (in micrograms per kg) associated with the observation (240, 190)? (Round your answers to two decimal places.) predicted value micrograms per kg residual micrograms per kg (c) Look again at the scatterplot from part (a). Which observation is potentially influential? Explain the reason for your choice. (Hint: See Example 4.9.) The observation (300, 135) is potentially influential because that point has an x value that is far away from the rest of the data set. The observation (240, 190) is potentially influential because that point has an x value that is far away from the rest of the data set. The observation (300, 135) is potentially influential because that point has a large residual. The observation (150, 150) is potentially influential because that point has a large residual. The observation (150, 150) is potentially influential because that point has an x value that is far away from the rest of the data set. (d) When the potentially influential observation is deleted from the data set, the equation of the least squares regression line fit to the remaining five observations is = -44 + 0.83x. Use this equation to predict acrylamide concentration (in micrograms per kg) for a frying time of 240 seconds. 155.2 micrograms per kg Compare this prediction to the prediction made in part (b).
webassign.net
Content
W STA 1053 HW10 Module10 - STA 1053.003 Spri...
b Acrylamide is a chemical that is sometimes f...
calculator - Google Search
+
Your best submission for each question part is used for your score.
5.
DETAILS
PREVIOUS ANSWERS
MY NOTES
ASK YOUR TEACHER
PRACTICE ANOTHER
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression
Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"t describes a study to investigate the effect of x = frying time
(in seconds) and y = acrylamide concentration (in micrograms per kg) in french fries. The data in the accompanying table are approximate values read from a graph that
appeared in the paper.
Frying
Time
Acrylamide
Concentration
150
150
240
120
240
190
270
185
300
135
300
275
(a) Construct a scatterplot of these data.
y
350
y
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Transcribed Image Text:webassign.net Content W STA 1053 HW10 Module10 - STA 1053.003 Spri... b Acrylamide is a chemical that is sometimes f... calculator - Google Search + Your best submission for each question part is used for your score. 5. DETAILS PREVIOUS ANSWERS MY NOTES ASK YOUR TEACHER PRACTICE ANOTHER Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"t describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. Frying Time Acrylamide Concentration 150 150 240 120 240 190 270 185 300 135 300 275 (a) Construct a scatterplot of these data. y 350 y 350 300 300 250 250 200 200 150 150 100 100 50
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